Data management interfaces and tools have been developed to help data scientists analyze data. For instance, graphing tools can be used to visually represent underlying data that is stored in a variety of formats and locations.
Some data sets are very complex, however, being stored in disparate formats and locations. This can make it difficult to intuitively process and understand the correlations that exist between the underlying data. Accordingly, it is sometimes necessary to apply one or more data transforms to the data in order to modify the data into a more unified and comprehensible format for subsequent analysis.
Data scientists transform the data with discrete tasks. These tasks can include simple algorithms such as multiplication or addition. They can also include complex algorithms for parsing, splitting, normalizing, merging, reformatting or for performing other complex transformations on the data.
In order to process complex data sets, it is often necessary for a data scientist to build a customized transformation pipeline that includes a plurality of tasks that are specifically sequenced for modifying the data contained in the target data set, based on the particular attributes of the data set, as well as the attributes of other tasks that are sequenced in the transformation pipeline for modifying the data in the desired way. For instance, certain tasks will have input requirements that require the data to be of a certain type before it can be processed to generate specific types of output. Furthermore, while some tasks may be executed independently, other tasks are co-dependent and can only be executed in combination with one or more other tasks that are performed in a particular sequence. Accordingly, significant amounts of time are spent identifying the right combination of tasks to assemble and how those tasks should be sequenced.
Because a transformation pipeline will often include unique combinations and sequences of tasks that are specifically designed to process data sets having particular attributes, they are not very fungible, meaning it is difficult to apply one transformation pipeline that is designed for one data set having a first set of attributes to another data set having different attributes, inasmuch as the new data set attributes may not be compatible with the requirements of the tasks in the transformation pipeline. Additionally, when the transformation pipeline is applied to the same data set in a different domain (e.g., at a different time, in a different session, on a different platform), the attributes of the data may be updated or modified in such a way as to render the transformation pipeline incompatible or inoperable for its original purposes.
Notwithstanding the foregoing customization requirements for processing certain data sets, it is still common practice for a data scientist to attempt to leverage some of the functionality of an existing transformation pipeline, rather than building a new transformation pipeline from scratch. One reason for this is because it can be incredibly difficult to build a transformation pipeline from scratch, as described above. The data scientist may also recognize similarities that exist between the target data set of the original domain and the target data set of the new domain.
Unfortunately, even when similarities exist between different data sets, it can still be difficult to know whether the transformation pipeline will be compatible with the new target data set without first executing the transformation pipeline on the new data set. Furthermore, if and when incompatibility/operability problems surface, it can be difficult to diagnose which specific tasks in the transformation pipeline are experiencing or creating the incompatibility/operability issues as the transformation pipeline is applied to the new domain (e.g., new/updated data set, in a new session and/or on a new platform with new execution parameters).
In order to identify the incompatibility/operability issues, it is often necessary for the data scientists to iteratively modify and execute the transformation pipeline in the new domain until the problems are ultimately diagnosed and resolved. This is similar to the tinkering and experimentation that is required when designing and testing a transformation pipeline from scratch.
The difficulties in diagnosing and adjusting transformation pipelines for disparate data sets and/or other domains is a technical problem that results in a significant waste of resources (e.g., time and computer processing). Accordingly, there continues to be an ongoing need for improved systems and tools for facilitating the manner in which transformation pipelines (such as the actionable task structures described herein) are evaluated and adjusted for application to disparate data sets and/or other domains.